Journal article 1117 views
Bayesian identification of bacterial strains from sequencing data
Aravind Sankar,
Sion C. Bayliss,
Edward J. Feil,
Jukka Corander,
Ben Pascoe
,
Guillaume Méric,
Antti Honkela,
Matthew Hitchings
,
Brandon Malone,
Samuel K. Sheppard
Microbial Genomics, Volume: 2, Issue: 8
Swansea University Authors:
Ben Pascoe , Matthew Hitchings
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DOI (Published version): 10.1099/mgen.0.000075
Abstract
Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate re...
Published in: | Microbial Genomics |
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ISSN: | 2057-5858 2057-5858 |
Published: |
2016
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URI: | https://cronfa.swan.ac.uk/Record/cronfa26788 |
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2018-10-08T12:37:15.8610708 v2 26788 2016-03-21 Bayesian identification of bacterial strains from sequencing data 4660c0eb7e6bfd796cd749ae713ea558 0000-0001-6376-5121 Ben Pascoe Ben Pascoe true false be98847c72c14a731c4a6b7bc02b3bcf 0000-0002-5527-4709 Matthew Hitchings Matthew Hitchings true false 2016-03-21 PMSC Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at this https URL Journal Article Microbial Genomics 2 8 2057-5858 2057-5858 31 12 2016 2016-12-31 10.1099/mgen.0.000075 COLLEGE NANME Medicine COLLEGE CODE PMSC Swansea University 2018-10-08T12:37:15.8610708 2016-03-21T08:44:44.6105154 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Aravind Sankar 1 Sion C. Bayliss 2 Edward J. Feil 3 Jukka Corander 4 Ben Pascoe 0000-0001-6376-5121 5 Guillaume Méric 6 Antti Honkela 7 Matthew Hitchings 0000-0002-5527-4709 8 Brandon Malone 9 Samuel K. Sheppard 10 |
title |
Bayesian identification of bacterial strains from sequencing data |
spellingShingle |
Bayesian identification of bacterial strains from sequencing data Ben Pascoe Matthew Hitchings |
title_short |
Bayesian identification of bacterial strains from sequencing data |
title_full |
Bayesian identification of bacterial strains from sequencing data |
title_fullStr |
Bayesian identification of bacterial strains from sequencing data |
title_full_unstemmed |
Bayesian identification of bacterial strains from sequencing data |
title_sort |
Bayesian identification of bacterial strains from sequencing data |
author_id_str_mv |
4660c0eb7e6bfd796cd749ae713ea558 be98847c72c14a731c4a6b7bc02b3bcf |
author_id_fullname_str_mv |
4660c0eb7e6bfd796cd749ae713ea558_***_Ben Pascoe be98847c72c14a731c4a6b7bc02b3bcf_***_Matthew Hitchings |
author |
Ben Pascoe Matthew Hitchings |
author2 |
Aravind Sankar Sion C. Bayliss Edward J. Feil Jukka Corander Ben Pascoe Guillaume Méric Antti Honkela Matthew Hitchings Brandon Malone Samuel K. Sheppard |
format |
Journal article |
container_title |
Microbial Genomics |
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2 |
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8 |
publishDate |
2016 |
institution |
Swansea University |
issn |
2057-5858 2057-5858 |
doi_str_mv |
10.1099/mgen.0.000075 |
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Faculty of Medicine, Health and Life Sciences |
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facultyofmedicinehealthandlifesciences |
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Faculty of Medicine, Health and Life Sciences |
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facultyofmedicinehealthandlifesciences |
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Faculty of Medicine, Health and Life Sciences |
department_str |
Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine |
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description |
Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at this https URL |
published_date |
2016-12-31T03:32:15Z |
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1763751321022758912 |
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11.021648 |